Transfer Learning in Deep Reinforcement Learning

Abstract

Reinforcement learning has quickly risen in popularity because of its simple, intuitive nature and it's powerful results. In this paper, we study a number of reinforcement learning algorithms, ranging from asynchronous q-learning to deep reinforcement learning. We focus on the improvements they provide over standard reinforcement learning algorithms, as well as the impact of initial start-ing conditions on the performance of a reinforcement learning agent